Case Study — Developing an AI Strategy and Roadmap

1. Organisational Problem — Lack of Cohesive AI Strategic Direction

The organisation was a UK-based professional services firm with an established digital environment, a strong client delivery focus and growing interest in how artificial intelligence could improve efficiency, knowledge work and service delivery. Senior leadership recognised the opportunity, but also the need to manage organisational, professional and governance risks carefully.

A prior AI Capability and Maturity Assessment established that the organisation was operating at Maturity Level 2 (Developing), with gaps in governance, organisational understanding and strategic alignment. While individual teams had begun exploring AI opportunities, these activities were not yet connected through a coherent organisation-wide strategy.

Without a clear strategic direction, leadership risked fragmented, inconsistently governed AI activity that was unlikely to deliver sustained value. Leadership therefore faced a critical question:

"How do we move from fragmented AI activity to a structured, organisation-wide approach that delivers measurable value?"

In the Orr Consulting AI Transformation Process, this type of engagement typically sits within the Design stage, helping organisations translate discovery insights into a structured AI Strategy and Roadmap before moving into detailed business cases and implementation.

2. Situation

The board had expressed a clear ambition to use AI in ways that would improve business performance while maintaining appropriate governance and control. Leadership wanted AI adoption to support operational efficiency, service quality, competitive positioning and responsible innovation.

However, without a defined AI strategy, there was a risk that:

• AI initiatives would remain fragmented

• investment decisions would lack consistency

• governance gaps would persist

• opportunities to scale AI would be missed

3. Background

Following the Capability and Maturity Assessment, the board set a target of progressing to Maturity Level 4 — Leading Capability.

To support this, Orr Consulting was engaged to help shape a structured AI Strategy and Roadmap aligned to organisational objectives and informed by prior discovery work.

4. Action Taken — AI Strategy Development

Orr Consulting worked with senior managers to develop a structured AI Strategy and Roadmap, while ensuring that the organisation retained ownership of both the outputs and the delivery decisions.

The work followed the methodology set out in the Insight Developing a Successful AI Strategy and Roadmap.

5. The AI Strategy and Roadmap

5.1 AI Vision Statement

The AI Vision Statement defined the organisation's desired future state:

The AI vision is to embed AI across our core service delivery, knowledge management and decision-support activities in ways that measurably improve efficiency, quality and scalability. AI will be delivered as a practical enabler of business performance rather than a standalone technology initiative. Its adoption will be shaped by organisational need, strong governance, professional standards and appropriate organisational control.

5.2 Strategic Context and Objectives

The organisation’s strategic objectives were to improve operational efficiency, enhance client experience, strengthen knowledge management and reuse, enable data-supported decision-making and ensure responsible adoption of AI. AI was positioned as an enabler of these objectives rather than a standalone initiative.

5.3 Current State Assessment

The Capability and Maturity Assessment showed relatively stronger data readiness at Level 3 (Established), but Governance and Assurance at Level 1 (Initial), with most other areas at Level 2 (Developing). This indicated that structured adoption was feasible, but not yet ready to scale safely.

5.4 Future State — Target Capabilities and Use Cases

5.4.1 Target Capabilities

The target future state included stronger governance and assurance, greater organisation-wide understanding of AI, scalable delivery capability and more integrated use of AI across knowledge management, client engagement and decision-support activities.

5.4.2 Prioritised AI Use Cases

Based on structured AI Use Case Discovery, priority use cases focused on knowledge and service delivery, operational efficiency, client engagement and decision support. Early priorities included AI-assisted drafting, document summarisation, semantic search, meeting summarisation, bid support and executive briefing preparation. These were prioritised based on strategic alignment, practical deliverability, expected value and data readiness. Not all were expected to progress at the same pace, with some suitable for early pilots and others dependent on stronger governance, capability and delivery foundations.

5.5 Capability Gap Analysis

Comparison of the current and target states identified four priority gaps: Governance and Assurance, Education and Training, Delivery Capability and Strategy and Alignment. Without addressing these, the organisation would be unlikely to scale AI safely or realise consistent value. These gaps informed the strategic priorities.

5.6 Strategic Priorities and Initiatives

Three strategic priorities were defined.

• Establish AI Governance and Control - Implement an AI governance and assurance framework, supported by acceptable use policies and risk assessment processes.

• Build Organisational AI Capability - Strengthen leadership understanding, targeted education and delivery capability for AI-enabled initiatives.

• Deliver High-Value AI Use Cases - Prioritise low-complexity, high-value use cases and use early pilots to establish reusable patterns for scaling.

5.7 Indicative Costs and Investment Profile

At strategy stage, investment requirements were assessed at a high level based on prioritised use cases, delivery complexity, capability gaps and the scale of governance, integration and organisational change required. The analysis indicated that investment would be driven primarily by capability development, governance, integration and change, rather than by technology licence costs alone. Overall, investment was expected to be moderate and phased, with initial focus on governance and assurance development, leadership capability building and targeted pilot delivery.

This gave leadership an order-of-magnitude view of likely investment requirements, enabling the board to assess the affordability and strategic viability of the roadmap. The strategy could therefore be considered for approval on an informed basis, while more detailed investment decisions would remain subject to subsequent programme and project business cases.

5.8 High-Level Delivery Risks and Mitigations

High-Level Risk	Mitigation and Roadmap Response
R1 Governance and assurance risk — AI adoption could outpace governance arrangements, creating exposure in relation to data handling, client confidentiality and regulatory expectations.	Establish an AI governance and assurance framework, including acceptable use policies and oversight arrangements. Reflected in Phase 1 — Foundations.
R2 Delivery capability risk — The organisation did not yet have an established capability for delivering and scaling AI-enabled initiatives.	Build delivery capability through targeted leadership support, defined delivery approaches and early pilot activity. Reflected across Phase 1 and Phase 2.
R3 Data readiness risk — Data quality, availability and accessibility varied across prioritised use cases, affecting feasibility and value realisation.	Assess data readiness at use-case level and prioritise early pilots where data requirements were more manageable. Reflected in the sequencing of pilot activity and later scaling decisions.

5.9 Roadmap and Phasing

The roadmap was structured into three phases.

• Phase 1 — Foundations (0–3 months) - Governance framework implementation, acceptable use policy development, leadership education and initiation of selected pilots.

• Phase 2 — Pilot and Scale (3–9 months) - Expansion of pilot use cases, knowledge management enhancements and refinement of governance and assurance processes.

• Phase 3 — Embed and Optimise (9–18 months) - Scaling of successful use cases, integration into core service delivery and continuous optimisation of capability and governance.

5.10 Benefits and Outcomes

Expected benefits included improved efficiency in knowledge work and service delivery, better access to organisational knowledge and reduced risk through stronger governance and oversight.

6. Recommended Next Steps

The board approved the strategy and agreed to initiate Phase 1 delivery, with immediate focus on governance, capability development and targeted pilot implementation. Executive accountability was assigned to the Chief Operating Officer, supported by the senior leadership team. These actions were designed to support progression to Maturity Level 4 — Leading Capability.

7. Final Thoughts

Many organisations recognise the potential of AI but struggle to translate this into a coherent and actionable strategy. An AI Strategy and Roadmap provides the structure needed to move from fragmented experimentation to coordinated transformation.

For this organisation, the strategy created a practical bridge between early AI interest, prioritised use cases and the governance and capability improvements needed to scale adoption responsibly. It also provided a clear pathway from Developing Capability (Level 2) to Leading Capability (Level 4), enabling the organisation to realise value while maintaining appropriate control and governance.

This Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.

8. Call to Action

If your organisation is exploring AI but lacks a clear strategic direction, an AI Strategy and Roadmap can provide a structured path forward.

If this case study reflects your organisation’s experience, Orr Consulting would be pleased to discuss your next AI steps.

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